AFLL: Real-time Load Stabilization for MMO Game Servers Based on Circular Causality Learning

TL;DR

AFLL is a real-time load stabilization system for MMO game servers that uses circular causality learning to adaptively throttle messages, significantly reducing server load while maintaining gameplay quality.

Contribution

This paper introduces AFLL, a novel real-time causal learning approach that dynamically adjusts message priorities to prevent overload in MMO servers.

Findings

Reduced CPU time by 48.3% on average

Lowered peak CPU time by 51.7%

Achieved zero learning overhead with high reproducibility

Abstract

Massively Multiplayer Online (MMO) game servers must handle thousands of simultaneous players while maintaining sub-100ms response times. When server load exceeds capacity, traditional approaches either uniformly throttle all message types regardless of importance (damaging gameplay) or apply fixed heuristic rules that fail to adapt to dynamic workloads. This paper presents AFLL (Adaptive Feedback Loop Learning), a real-time load stabilization system that learns the causal relationship between outgoing server messages and subsequent incoming client requests. AFLL employs backpropagation to continuously adjust message type weights, enabling predictive throttling that blocks low-priority messages before overload occurs while guaranteeing critical message delivery. Through controlled experiments with 1,000 concurrent players, AFLL reduced average CPU time by 48.3% (13.2ms to 6.8ms), peak CPU time by 51.7% (54.0ms to 26.1ms), and thread contention by 64.4% (19.6% to 7.0%), while maintaining zero learning overhead through background computation and caching optimizations. The system achieved remarkable reproducibility (CV < 2% across all metrics) and identified a three-stage causal chain linking message blocking to load reduction. AFLL demonstrates that circular causality learning enables practical real-time adaptation for latency-critical systems.